用孪生网络实现自监督快速MRI重建，精度达当前最优。
Exploring Siamese Networks in Self-Supervised Fast MRI Reconstruction
- 设计孪生结构，通过交替优化生成有效监督信号
- 在脑部和膝关节MRI上均达到自监督领域最好效果
- 适合医学影像重建、无参考数据训练场景
从欠采样k空间数据中使用深度神经网络重建MR图像，无需全采样训练参考数据，在实践中具有重要意义，属于自监督回归问题，需要有效的先验知识与监督。孪生网络基于“不变性”定义，已在无监督视觉表征学习中表现优异。构建同源变换图像并避免平凡解是基于孪生模型的两大挑战。本文探索了自监督训练范式下的孪生架构用于MRI重建，命名为SiamRecon。我们发现所提方法模拟了期望最大化算法，交替优化提供有效监督信号并防止模型坍缩。SiamRecon在单线圈脑部MRI和多线圈膝关节MRI上均实现了自监督学习领域的最先进重建精度。
原文摘要 · Abstract (English)
Reconstructing MR images using deep neural networks from undersampled k-space data without using fully sampled training references offers significant value in practice, which is a self-supervised regression problem calling for effective prior knowledge and supervision. The Siamese architectures are motivated by the definition "invariance" and shows promising results in unsupervised visual representative learning. Building homologous transformed images and avoiding trivial solutions are two major challenges in Siamese-based self-supervised model. In this work, we explore Siamese architecture for MRI reconstruction in a self-supervised training fashion called SiamRecon. We show the proposed approach mimics an expectation maximization algorithm. The alternative optimization provide effective supervision signal and avoid collapse. The proposed SiamRecon achieves the state-of-the-art reconstruction accuracy in the field of self-supervised learning on both single-coil brain MRI and multi-coil knee MRI.
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